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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...

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Updated: May 29, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Expected-depth selection for interpretable decision trees.

Zhigao Huang1, Yuzhuo Pan1, Miao Pan2

  • 1Key Laboratory of Information Functional Material for Fujian Higher Education, Quanzhou Normal University, Quanzhou, 362000, China.

Scientific Reports
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces soft-depth (Soft-D) selection for decision trees, optimizing for average inference cost (expected depth) instead of worst-case measures. Soft-D significantly reduces expected depth while maintaining accuracy, improving model efficiency.

Keywords:
Average-case complexityCost-sensitive inferenceDecision treesExplainable data miningInterpretabilityModel selectionPruning

Related Experiment Videos

Last Updated: May 29, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Traditional decision tree selection relies on worst-case metrics like maximum depth.
  • Model deployment often incurs costs based on the specific root-to-leaf path used for inference.
  • Expected depth, representing the average inference burden, is a more relevant metric for many applications.

Purpose of the Study:

  • To introduce and evaluate a novel method for selecting decision trees based on expected depth.
  • To compare the proposed soft-depth (Soft-D) selector against existing methods like CART and accuracy-first cost-complexity pruning (CCP).
  • To analyze the impact of expected depth selection on model accuracy and complexity.

Main Methods:

  • The soft-depth (Soft-D) selector identifies trees with minimum validation expected depth within an accuracy tolerance of the best candidate.
  • Evaluation across eleven benchmark datasets with 25 outer test folds per dataset.
  • Comparison with unpruned CART, accuracy-first CCP, and a new depth-limited CART baseline.

Main Results:

  • Soft-D reduced expected depth by 47.4% compared to unpruned CART and 12.1% compared to accuracy-first CCP.
  • The gains were most significant when a practitioner-selected maximum depth still allowed for long paths.
  • Analysis confirmed expected depth selection as a viable deployment-time strategy, distinct from global tree induction optimization.

Conclusions:

  • Expected depth selection offers a lightweight, deployment-time approach to optimize decision tree inference costs.
  • Soft-D provides a practical alternative to worst-case structural measures for interpretable decision trees.
  • The method effectively balances accuracy and average inference burden, enhancing practical model deployment.